Kina ka inganta aikin MongoDB?

Question

IT Interview Practice · Accepted Answer

Ingantawa aikin MongoDB ta saita ne a kan **indexing** (babbar nasara), **schema design** don tsarin access, efficient **queries**, da kuma amfani da kayan aiki kamar **`explain()`** don neman damuwa. A kaari: auna da farko, sannan gyara matsalar gaske — yawancin lokaci sune indexing da baki ko kallon schema da baki.

## Indexing shine babbar nasara

```js
// use explain() to check if a query uses an index
db.users.find({ email: "ann@x.com" }).explain("executionStats");
// → look for: COLLSCAN (collection scan = BAD, no index) vs IXSCAN (index = GOOD)

db.users.createIndex({ email: 1 });   // add the missing index → fast
```

```text
✓ Index fields used in queries, sorts, and filters (the #1 optimization)
✓ Compound indexes for multi-field queries (mind the field order)
✓ explain() to verify queries use indexes (COLLSCAN = problem)
✓ Don't over-index (slows writes); index what's actually queried
```

## Schema design don tsarin access

```text
✓ EMBED data accessed together → single-query reads (no joins)
✓ Reference large/unbounded/shared data appropriately
✓ Design around your QUERIES (the access-pattern principle) → most reads = one document
→ Poor schema design (e.g. needing many $lookups, huge documents) hurts performance
  more than almost anything else in MongoDB.
```

## Ingantawa request

```text
✓ Use PROJECTION — return only needed fields (less data transferred)
✓ Filter early; use covered queries (all fields from the index → no document fetch)
✓ In aggregation pipelines, put $match (and $limit) EARLY to reduce data through the pipeline
✓ Avoid querying without the shard key on sharded collections (scatter-gather)
✓ Use limit/pagination; prefer range-based over deep skip()
```

## Wasu hanyoyi

```text
✓ Working set in RAM — frequently-accessed data + indexes should fit in memory
  (MongoDB caches in RAM; disk access is much slower)
✓ Connection pooling (drivers pool by default)
✓ Monitor: explain(), MongoDB profiler, Atlas Performance Advisor (suggests indexes)
✓ Avoid large/unbounded arrays; consider the bucket pattern for time-series
```

## Me ya sa ya matuwa

Ingantawa aikin MongoDB ilmi ne mai mahimmanci wajen babbar aiki, domin kiyaye aikiken MongoDB da sauri, kuma fahimtar hanyar daidai — auna-tushe, tare da indexing da schema design a matsayi na gaba — mahimmaci ne don ingantawa mai tasiri.

Babbar nasara ta farko ita ce **indexing** (iri guda da database masu yawa): indexing da babu yana haifar da **collection scans** (COLLSCAN — duba kowane gida, saurin da saurin ajiye-ajiye yayin da bayanai ke karuwa), kuma saka indexing da daidai a kan fields da ake bugun bugun/rarraba/tsara ya zama sauri index scans — amfani da **`explain()`** don gamawa cewa request suna amfani da indexing (gano COLLSCAN matsaloli) shine mabukin gano mahimmanci. **Schema design don tsarin access** watakila ma yafi tasiri a MongoDB fiye da SQL: zane kewaye request (haɗa bayanai da aka shiga tare don karatu bugun bugun gida, haɗa da daidai) don yawancin karatu su kashe gida guda, yayin da zane maraya (butuwa da yawa `$lookup`, dokoki masu girma) yana cutar da aiki kaifa — sanya schema design gabas babbar ingantawa hanya musamman ga MongoDB na tayarwa. **Ingantawa request** (projection don dawo da filayen da ake buka kawai, covered queries, sanya `$match`/`$limit` a karfe a cikin aggregation pipelines don ragewa numfashi, kauce scatter-gather akan nada collections, paging mai aiki) da kuma juyawa amfani set ya dace a RAM (bayanai masu amfani da indexing rufe a ajiye-ajiye, tun faifai nadi ce mai saurin da saurin) suna mikawa gajerun kayan aiki.

Turumin **auna-tushe** (amfani da explain(), masu siffanta, da Atlas Performance Advisor don nema da gyara damuwa na gaske — yawancin lokaci sune babu indexing ko maraya schema) shine mahimmaci.

Tun aikin MongoDB mahimmanci ne don aikiken da kuma ingantawa daidai (indexing, schema design, efficiency request, working set a RAM) shine abin da ya kiyaye saurin aiki a scale, fahimtar ingantawa aikin MongoDB — musamman indexing (gamawa tare da explain()) da schema design na tunawa pattern a matsayi na gaba shine babbar hanyoyi — ilmi ne mai mahimmanci wajen babbar aiki don aiki da MongoDB mabura, abubuwan da aka saba tunawa da jiya kuma bugun bugun tunawa da za'i nuni iya saita MongoDB da tsari.

Indexing shine babbar nasara

Schema design don tsarin access

Ingantawa request

Wasu hanyoyi

Me ya sa ya matuwa

Tambayoyi masu alaƙa